Abstract:
Amorphous computing consists of a multitude of interacting computers with modest computing power and memory, and modules for intercommunication. These collections of devices are known as swarms. The desired coherent global behavior of the computer is achieved from the local interactions between the individual agents. The global behavior of these vast numbers of unreliable agents is resilient to a small fraction of misbehaving agents and noisy and intimidating environment. This makes them highly useful for sensor networks, MEMS, internet nodes, etc. The ideas for amorphous computing have been derived from swarm behavior of social organisms like the ants, bees and bacteria. A certain level of intelligence, exceeding those of the individual agents, results from the swarm behavior. Swarm Intelligence may be derived from the randomness, repulsion and unpredictability of the agents, thereby resulting in diverse solutions to the problem. There are no known criteria to evaluate swarm intelligence performance. Swarm Intelligence relies stigmergic principles in order to solve complex problems using only simple agents ,Amorphous computing consists of a multitude of interacting computers with modest computing power and memory, and modules for intercommunication. These collections of devices are known as swarms. The desired coherent global behavior of the computer is achieved from the local interactions between the individual agents. The global behavior of these vast numbers of unreliable agents is resilient to a small fraction of misbehaving agents and noisy and intimidating environment. This makes them highly useful for sensor networks, MEMS, internet nodes, etc. Presently, of the 8 billion computational units existing worldwide, only 2% of them are stand-alone computers. This proportion is project and implimentationed to further decrease with the paradigm shift to the biologically inspired amorphous computing model. An insight into amorphous and swarm computing will be given in this paper. The ideas for amorphous computing have been derived from swarm behaviors of social organisms like the ants, bees and bacteria. Recently, biologists and computer scientists studying artificial life have modeled biological swarms to understand how such social animals interact, achieve goals and evolve. A certain level of intelligence, exceeding those of the individual agents, results from the swarm behavior. Amorphous Computing is a established with a collection of computing particles -with modest memory and computing power- spread out over a geographical space and running identical programs. Swarm Intelligence may be derived from the randomness, repulsion and unpredictability of the agents, thereby resulting in diverse solutions to the problem. There are no known criteria to evaluate swarm intelligence performance; the development of swarm computing has been instilled by some of the natural phenomenon. The most complex of the activities, like optimal path finding, have been executed by simple organisms. Lately MEMS research has paved the way for manufacturing the swarm agents with low costs and high efficiency. In case of the ant colonies, the worker ants have decentralized control and a robust mechanism for some of the complex activities like foraging, finding the shortest path to food source and back home, build and protect nests and finding the richest food source in the locality. The ants communicate by using pheromones. Trails of pheromone are laid down by a given ant, which can be followed by other ants. Depending on the species, ants lay trails traveling from the nest, to the nest or possibly in both directions. Pheromones evaporate over time. Pheromones also accumulate with multiple ants using the same path. As the ants forage, the optimal path to food is likely to have the highest deposition of pheromones, as more number of ants follow this path and deposit pheromones. The longer paths are less likely to be traveled and therefore have only a smaller concentration of pheromones. With time, most of the ants follow the optimal path. When the food sources deplete, the pheromones evaporate and new trails can be discovered. This optimal path finding approach has a highly dynamic and robust nature. Similar organization and behaviour are also present in the flocks of bird. For a bird to participate in a flock, it only adjusts its movements to coordinate with the movements of its flock mates, typically its neighbours that are close to it in the flock. A bird in a flock simply tries to stay close to its neighbours, but avoid collisions with them. Each bird does not take commands from any leader bird since there is no lead bird. Any bird can Ã‚Â°y in the front, center and back of the swarm. Swarm behaviour helps birds take advantage of several things including protection from predators (especially for birds in the middle of the flock), and searching for food (essentially each bird is exploiting the eyes of every other bird). Even complex biological entities like brain are a swarm of interacting simple agents like the neurons. Each neuron does not have the holistic picture, but processes simple elements through its interaction with few other neurons and paves way for the thinking process